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Data Science Fundamentals Part 1

Unit 2

Pearson

This course dives into real-world data sourcing, including making web requests, web scraping, and integrating diverse data types from APIs, files, and databases. You'll learn to parse and structure data in formats like XML and JSON, and leverage object-oriented programming to create robust data models. By the end of the course, you’ll be equipped to efficiently acquire, transform, and prepare data for advanced analysis.

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Learners who complete Data Science Fundamentals Part 1: Unit 2 will develop knowledge and skills that may be useful to these careers:

Data Engineer

A Data Engineer builds and maintains the robust infrastructure that allows organizations to collect, process, and store vast amounts of information. This includes designing scalable data pipelines and integrating diverse data sources. For an aspiring Data Engineer, this course offers essential training in modern data acquisition techniques, including web scraping, making web requests, and leveraging APIs. You will learn to integrate various data types, parse and structure formats like XML and JSON, and apply object-oriented programming to create resilient data models. Mastering these skills, particularly the Extract Transform Load pipeline, is fundamental to preparing data for advanced analysis and ensuring data quality across an organization.

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ETL Developer

An ETL Developer specializes in the Extract Transform Load process, building pipelines that move data from various sources, transform it into a usable format, and load it into target systems for analysis and reporting. This course is exceptionally well-suited for an aspiring ETL Developer, as it focuses intensely on the core competencies required for this role. You will gain hands-on experience in sourcing data from web requests, web scraping, APIs, files, and databases. Learning to parse and structure diverse data types such as XML and JSON, along with leveraging object-oriented programming for data models, directly prepares you to design and implement efficient, robust data transformation solutions.

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Software Engineer Data Focus

A Software Engineer with a data focus designs and implements the software systems and infrastructure that handle data at scale, from acquisition to processing and storage. This course is exceptionally valuable for a Software Engineer seeking to specialize in data. It provides in-depth training on data sourcing techniques like web requests, web scraping, and integrating data from APIs and databases. You will learn to parse and structure complex data formats such as XML and JSON, and crucially, apply object-oriented programming to build robust data models. These transferable skills are essential for constructing efficient, scalable, and reliable data-driven applications and systems.

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Integration Specialist

An Integration Specialist focuses on connecting disparate software systems and data sources, ensuring seamless data flow and communication across an enterprise. This course is perfectly aligned with the needs of an Integration Specialist. You will acquire hands-on experience in making web requests, web scraping, and integrating diverse data types from APIs, files, and databases. The ability to parse and structure data in various formats like XML and JSON is crucial for this role, as is leveraging object-oriented programming to create data models. These skills are fundamental to building robust, reliable, and efficient system integrations.

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Data Architect

A Data Architect designs an organization's overall data strategy, including data flow, storage, and processing. This role typically requires an advanced degree. This course is highly relevant for an aspiring Data Architect because it imparts foundational skills in data acquisition and modeling. You will learn to integrate diverse data types from web requests, APIs, files, and databases, and understand how to manage data lineage. Proficiency in parsing and structuring data in formats like XML and JSON, coupled with leveraging object-oriented programming for data models, is essential for designing robust, scalable, and efficient data ecosystems that support organizational needs.

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Data Quality Analyst

A Data Quality Analyst is responsible for ensuring the accuracy, completeness, and consistency of an organization's data, which is paramount for reliable decision-making. This course is highly relevant for an aspiring Data Quality Analyst. It deepens your understanding of where data originates and how it is processed. You will gain skills in making web requests, web scraping, and integrating data from diverse sources like APIs and files. Critically, learning to parse and structure data in formats such as XML and JSON, alongside managing data lineage, directly empowers you to identify potential data quality issues at their source and implement effective remediation strategies.

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Business Intelligence Developer

A Business Intelligence Developer creates interactive dashboards, reports, and data visualizations that translate raw data into actionable insights for stakeholders. The quality of these insights depends entirely on the underlying data, making data preparation skills invaluable. This course significantly aids an aspiring Business Intelligence Developer by equipping you with the foundational knowledge of data acquisition and transformation. You will learn to source data from diverse platforms, including web requests, APIs, and databases, and understand how to parse and structure data in formats like XML and JSON. This proficiency in the Extract Transform Load pipeline ensures that the data used for BI tools is accurate, consistent, and ready for effective reporting.

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Solutions Architect Data Focus

A Solutions Architect specializing in data designs comprehensive data systems and strategies that align with an organization's business objectives. This role typically requires an advanced degree. This course is highly beneficial for an aspiring Solutions Architect Data Focus, as it provides a solid foundation in the initial and critical stages of data architecture. You will delve into real-world data sourcing, including web requests, web scraping, and integrating diverse data types from APIs and databases. Mastering the parsing and structuring of data in formats such as XML and JSON, and leveraging object-oriented programming for data models, is crucial for designing scalable, efficient, and robust data solutions.

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Data Governance Specialist

A Data Governance Specialist establishes and enforces policies, standards, and practices to ensure the quality, security, and usability of an organization’s data assets. Understanding the entire data lifecycle is critical for this role. This course strongly supports an aspiring Data Governance Specialist by providing foundational knowledge of data acquisition and transformation. You will learn about data sourcing, including web requests and integrating data from various platforms. The emphasis on managing data lineage and correctly parsing and structuring diverse data formats like XML and JSON directly informs how to define and implement rules for data integrity, compliance, and effective data stewardship.

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Data Analyst

A Data Analyst interprets complex datasets to identify trends, patterns, and insights that drive business decisions. While often focused on analysis, a strong understanding of data provenance and structure is crucial for accurate interpretation. This course can help a Data Analyst by providing a deep dive into how data is sourced and prepared. You will learn about making web requests, web scraping, and integrating data from APIs and databases. Understanding how data is parsed and structured in formats like XML and JSON ensures you can critically evaluate data quality and reliability, enhancing your ability to perform rigorous analysis and communicate findings with confidence.

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Technical Project Manager Data Focus

A Technical Project Manager with a data focus leads and oversees data-intensive projects, requiring a solid grasp of the technical processes involved. This course helps a Technical Project Manager Data Focus by providing an essential understanding of data acquisition and transformation. You will explore methods for sourcing and extracting data from various platforms, including web requests and working with APIs and databases. Understanding how data is parsed and structured in formats like XML and JSON, and the principles of the Extract Transform Load pipeline, enables you to effectively plan, scope, and manage data projects, anticipate challenges, and communicate intelligently with technical teams.

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Application Programming Interface Developer

An Application Programming Interface Developer designs, builds, and maintains the interfaces that allow different software systems to communicate and exchange data. This course is relevant for an Application Programming Interface Developer due to its strong emphasis on working with APIs and data structuring. You will gain practical experience in making web requests and integrating diverse data types, understanding the mechanics of how data is exchanged. The course specifically trains you to parse and structure data in common formats like XML and JSON, and to leverage object-oriented programming to create robust data models, which are fundamental skills for both consuming and developing effective APIs.

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Data Scientist

A Data Scientist extracts insights from vast datasets, builds predictive models, and communicates findings to inform strategic decisions. This role typically requires an advanced degree. For a Data Scientist, this course may be useful as it addresses a significant portion of their work: preparing data for analysis. You will learn efficient methods for data sourcing, including web requests, web scraping, and integrating data from APIs and databases. Mastering the parsing and structuring of data in formats such as XML and JSON and understanding the Extract Transform Load pipeline ensures you can consistently work with high-quality, well-structured data, which is a prerequisite for any advanced analytical endeavor.

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Database Administrator

A Database Administrator manages and maintains an organization's databases, ensuring their performance, security, and integrity. While not primarily focused on database administration per se, understanding how data enters and is structured within these systems is highly beneficial. For a Database Administrator, this course may be useful by providing insight into diverse data sourcing methods, including integrating data from APIs and various file types. Through learning to parse and structure data in formats like XML and JSON, and using object-oriented programming for data models, you enhance your comprehension of data lineage and schema design, contributing to better database architecture and data quality management.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys scalable machine learning systems. This role typically requires an advanced degree. While this course does not cover machine learning algorithms themselves, it may be useful for a Machine Learning Engineer by focusing on the absolutely critical upstream process of data preparation. You will gain skills in efficiently acquiring and transforming data from diverse sources, including web requests, web scraping, and APIs. Expertise in parsing and structuring data in formats like XML and JSON is foundational, as high-quality, well-prepared data is paramount for training robust and accurate machine learning models.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Science Fundamentals Part 1: Unit 2.

Clean Code

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While not solely about OOP, this book is fundamental for any programmer, including those using OOP. It emphasizes writing readable, maintainable, and well-structured code, which is essential for effective object-oriented design. It teaches the difference between good and bad code and how to refactor, providing practical examples and insights into SOLID design principles based on OOP concepts. This crucial reference for writing high-quality code in any object-oriented language.

Data Science Fundamentals Part 1

Two deals to help you save

What's inside

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